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Robustness in Sequence Modeling
Nathan Ng · Haoran Zhang · Vinith Suriyakumar · Chantal Shaib · Kyunghyun Cho · Yixuan Li · Alice Oh · Marzyeh Ghassemi

Fri Dec 02 07:00 AM -- 03:00 PM (PST) @ Room 290
Event URL: https://robustseq2022.github.io »

As machine learning models find increasing use in the real world, ensuring their safe and reliable deployment depends on ensuring their robustness to distribution shift. This is especially true for sequential data, which occurs naturally in various data domains such as natural language processing, healthcare, computational biology, and finance. However, building models for sequence data which are robust to distribution shifts presents a unique challenge. Sequential data are often discrete rather than continuous, exhibit difficult to characterize distributions, and can display a much greater range of types of distributional shifts. Although many methods for improving model robustness exist for imaging or tabular data, extending these methods to sequential data is a challenging research direction that often requires fundamentally different techniques.

This workshop aims to facilitate progress towards improving the distributional robustness of models trained on sequential data by bringing together researchers to tackle a wide variety of research questions including, but not limited to:
(1) How well do existing robustness methods work on sequential data, and why do they succeed or fail?
(2) How can we leverage the sequential nature of the data to develop novel and distributionally robust methods?
(3) How do we construct and utilize formalisms for distribution shifts in sequential data?

We hope that this workshop provides a first step towards improving the robustness, and ultimately safety and reliability, of models in sequential data domains.

Author Information

Nathan Ng (Massachusetts Institute of Technology)
Haoran Zhang (Massachusetts Institute of Technology)
Vinith Suriyakumar (Massachusetts Institute of Technology)
Chantal Shaib
Kyunghyun Cho (Genentech / NYU)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.

Yixuan Li (University of Wisconsin-Madison)
Alice Oh (KAIST)
Marzyeh Ghassemi (MIT)

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